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  • Introduction
  • Quickstart
  • Getting started
    • Install Feast
    • Create a feature repository
    • Deploy a feature store
    • Build a training dataset
    • Load data into the online store
    • Read features from the online store
  • Community
  • Roadmap
  • Changelog
  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
      • Google Cloud Platform
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
        • Azure AKS (with Terraform)
        • Google Cloud GKE (with Terraform)
        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
        • Feast CLI
      • Learn Feast
    • Concepts
      • Overview
      • Architecture
      • Entities
      • Sources
      • Feature Tables
      • Stores
    • Tutorials
      • Minimal Ride Hailing Example
    • User guide
      • Overview
      • Getting online features
      • Getting training features
      • Define and ingest features
      • Extending Feast
    • Reference
      • Configuration Reference
      • Feast and Spark
      • Metrics Reference
      • Limitations
      • API Reference
        • Go SDK
        • Java SDK
        • Core gRPC API
        • Python SDK
        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
      • Audit Logging
      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
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  • Offline (Historical) Store
  • Online Store

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  1. Feast on Kubernetes
  2. Concepts

Stores

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Last updated 3 years ago

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In Feast, a store is a database that is populated with feature data that will ultimately be served to models.

Offline (Historical) Store

The offline store maintains historical copies of feature values. These features are grouped and stored in feature tables. During retrieval of historical data, features are queries from these feature tables in order to produce training datasets.

Online Store

The online store maintains only the latest values for a specific feature.

  • Feature values are stored based on their

  • Feast currently supports Redis as an online store.

  • Online stores are meant for very high throughput writes from ingestion jobs and very low latency access to features during online serving.

Feast only supports a single online store in production

entity keys